Mahrami et al., 2016 - Google Patents
A hybrid metaheuritic technique developed for hourly load forecastingMahrami et al., 2016
View PDF- Document ID
- 3460640582988653164
- Author
- Mahrami M
- Rahmani R
- Seyedmahmoudian M
- Mashayekhi R
- Karimi H
- Hosseini E
- Publication year
- Publication venue
- Complexity
External Links
Snippet
Electricity load forecasting has become one of the most functioning tools in energy efficiency and load management and utility companies which has been made very complex due to deregulation. Due to the importance of providing a secure and economic electricty for the …
- 238000000034 method 0 title abstract description 40
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Semero et al. | EMD–PSO–ANFIS‐based hybrid approach for short‐term load forecasting in microgrids | |
Pousinho et al. | A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal | |
Chang et al. | An improved neural network-based approach for short-term wind speed and power forecast | |
Pousinho et al. | Short-term electricity prices forecasting in a competitive market by a hybrid PSO–ANFIS approach | |
Ardakani et al. | Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting | |
Ghods et al. | Methods for long-term electric load demand forecasting; a comprehensive investigation | |
Sujil et al. | FCM Clustering‐ANFIS‐based PV and wind generation forecasting agent for energy management in a smart microgrid | |
Grimaccia et al. | Neuro-fuzzy predictive model for PV energy production based on weather forecast | |
Cinar et al. | Development of future energy scenarios with intelligent algorithms: case of hydro in Turkey | |
Caputo et al. | Photovoltaic plants predictive model by means of ANN trained by a hybrid evolutionary algorithm | |
Kavousi-Fard et al. | Short term load forecasting of distribution systems by a new hybrid modified FA-backpropagation method | |
Zheng et al. | Short‐term wind power prediction in microgrids using a hybrid approach integrating genetic algorithm, particle swarm optimization, and adaptive neuro‐fuzzy inference systems | |
Semero et al. | An accurate very short-term electric load forecasting model with binary genetic algorithm based feature selection for microgrid applications | |
Banerjee et al. | Seeker optimization algorithm for load-tracking performance of an autonomous power system | |
Zhang et al. | Short-term load forecasting for microgrids based on DA-SVM | |
Samet et al. | Evaluation of neural network-based methodologies for wind speed forecasting | |
Khan et al. | Very short term load forecasting using Cartesian genetic programming evolved recurrent neural networks (CGPRNN) | |
Mahrami et al. | A hybrid metaheuritic technique developed for hourly load forecasting | |
Razak et al. | A novel hybrid method of LSSVM-GA with multiple stage optimization for electricity price forecasting | |
Niu et al. | Research on short-term power load time series forecasting model based on BP neural network | |
Pousinho et al. | Application of adaptive neuro‐fuzzy inference for wind power short‐term forecasting | |
Elamine et al. | Multi-agent system based on fuzzy control and prediction using NN for smart microgrid energy management | |
Park et al. | A comparison of neural network-based methods for load forecasting with selected input candidates | |
Dou et al. | Double‐deck optimal schedule of micro‐grid based on demand‐side response | |
Kumar et al. | Load forecasting of Andhra Pradesh grid using PSO, DE algorithms |